Introduction to Regularisation In Keras
Welcome to our comprehensive guide on Regularisation In Keras. This video is part of a series: https://sites.google.com/view/ml-basics/home.
Regularisation In Keras Comprehensive Overview
In this video, we explain the concept of In this week's #TidyTuesday video, I go over some common techniques to prevent overfitting neural networks. I demonstrate what ... We discuss the basic working of dropout - We show how the drop-out layer is added - It is demonstrated that using Fashion MNIST ...
Keras
Summary & Highlights for Regularisation In Keras
- We start by a gentle revisit of model overfitting,
- We're back with another deep learning explained series videos. In this video, we will learn about
- Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizing your model ...
- In this video, we go over initializers, activations, regularizers and constraints - all of which are essentially used to make layers ...
- Layer normalization, Filter response normalization (FRN), Thresholded linear unit (TLU), Normalizer-free networks, Gradient ...
In summary, understanding Regularisation In Keras gives us a better perspective.
